1 / 19

Expectations

Expectations. Adaptive Expectations Rational Expectations Modeling Economic Shocks. Let z t = value of variable z at time t, z e t+1 = expectation of z t+1 at time t. Perfect Foresight : Adaptive Expectations where is the “speed” of adjustment of expectations.

dinos
Download Presentation

Expectations

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Expectations Adaptive Expectations Rational Expectations Modeling Economic Shocks

  2. Let zt = value of variable z at time t, zet+1 = expectation of zt+1 at time t. • Perfect Foresight: • Adaptive Expectations where is the “speed” of adjustment of expectations. • Problem: Errors are systematic and repeated.

  3. Rational Expectations: The expectation of zt+1 at time t given all currently available information. (Statistical “conditional” expected value):

  4. Notes about Statistical Expectations • Let X = random variable • f(x) = Pr (X = x) = probability density of X • The expected value of X is (discrete) (continuous)

  5. Properties of Expected Value: For X and Y random variables and b constant: E(b) = b E(bX) = bE(X) E{ g(X) + h(X) } = E{g(X)} + E{h(X)} E{XY} = E(X)E(Y) + COV (X,Y)

  6. Let X and Y be random variables. • The conditional expectation of X given Y = y is given by where

  7. Modeling Economic Shocks • Many economic variables exhibit persistence: * If z is above (below) trend today, it will likely be above (below) trend tomorrow. • One way to model the idea of persistence of shocks is by an autoregressive (AR) process: where 0 < r < 1 measures the degree of persistence.

  8. Where e is a random “white noise” shock with mean zero: and constant variance. • r = 1  permanent shock to z, “random walk” r = 0  purely temporary shock, no persistence. 0 < r < 1  temporary but persistent Examples: Macroeconomic data: GDP, Money Supply, ect.

  9. Figure 3.2 Percentage Deviations from Trend in Real GDP from 1947--2003

  10. Monetary Policy: 2004 - 2008

  11. Numerical Example • Consider t = 20 periods • There is a one-time shock to et in period 1 where e1 = 10 and et = 0 for all other time periods:

  12. Notice the effect on ztdepends on the value of r which measures the amount of persistence for the shock e. r = 0  purely temporary r = 0.80  temporary but persistent

  13. r = 1  permanent

  14. Let’s use r = 0.80 for the shock to z. • Comparison of adaptive expectations (AE with a=0.5) and rational expectations (RE) of z. Actual value of z is in red, expected values for z are in blue. Adaptive Expectations Rational Expectations

  15. Rational expectations (RE) is the statistical forecast of future variables given all current information available at time t (Infot) • Notice since ztis known at time t: • With RE, the errors in expectations are random and average to zero: • When r = 1,  “Random Walk” or “Martingale”

  16. Application: Theory of Efficient Markets • If investors in stock markets have rational expectations, then the value of the stock market (z) should follow a random walk:  • Why? RE says that investors buy and sell based upon all information publicly available. I.e., the current stock price already reflects current public information.

  17. Implications: (i) Only unpredictable events cause stock market fluctuations. (ii) Market fluctuations cannot be systematically forecasted. Best to “follow” the market, cannot systematically “beat” the market.

More Related